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Articles 1 - 30 of 89
Full-Text Articles in Cognition and Perception
Beyond Core Object Recognition: Dnns As Models Of Dynamic Scene Perception, Matthias Tangemann, Matthias Kümmerer, Matthias Bethge
Beyond Core Object Recognition: Dnns As Models Of Dynamic Scene Perception, Matthias Tangemann, Matthias Kümmerer, Matthias Bethge
MODVIS Workshop
Deep neural networks (DNNs) have become influential computational models of human vision, particularly in explaining neural responses in the ventral stream. However, they frequently diverge from well-established findings in psychophysics, especially with regard to human perceptual biases, robustness, and generalization behavior. Much of the progress in aligning DNNs with human perception has focused on the task of core object recognition—the rapid identification of objects in static images (DiCarlo et al., 2012). In contrast, other critical dimensions of visual perception, such as motion processing and multi-object scene understanding, remain comparatively underexplored. In this talk, I present our recent work on modeling …
Effect Of Response Bias On Threshold Estimates In Confounded N-Alternative Optional Choice Experiments, James J. Blaha, Benjamin T. Backus, Thaddeus B. Czuba
Effect Of Response Bias On Threshold Estimates In Confounded N-Alternative Optional Choice Experiments, James J. Blaha, Benjamin T. Backus, Thaddeus B. Czuba
MODVIS Workshop
Three modifications of the familiar 2-AFC task can be useful when collecting data to estimate sensory thresholds from unpracticed participants: (1) Make responding optional, which relieves the participant from having to guess when they are uncertain; (2) Provide N>2 alternatives, which reduces the number of trials to estimate threshold; and (3) Confound the alternatives with an independent variable, for example by having participants choose visual field locations as alternatives when measuring threshold separately at each visual field location, which reduces the number of trials through multiplexing. The N-alternative optional-choice experiment (N-AOC) and its confounded variant have not been studied …
The N-Alternative Optional Choice Experiment, Benjamin T. Backus, James J. Blaha, Thaddeus B. Czuba
The N-Alternative Optional Choice Experiment, Benjamin T. Backus, James J. Blaha, Thaddeus B. Czuba
MODVIS Workshop
When testing visual function in clinical settings, one encounters test-takers who have difficulty following a forced-choice instruction when stimulus intensity is low. “I didn’t see anything, why do I have to guess?” An optional-choice paradigm makes it easier for these test-takers to provide data. Here we describe a theory of signal detection for N-alternative optional-choice (N-AOC) psychophysical tasks. The theory generalizes two well-known paradigms into a single framework: the theory for yes-no tasks is a degenerate case in which N=1, and the theory for N-alternative forced choice tasks (N-AFC) is a degenerate case in which the decision criterion is liberal. …
Bayesian Model Predicts Confidence In Perceptual Decision, Xinyi Yuan, Ralf M. Haefner
Bayesian Model Predicts Confidence In Perceptual Decision, Xinyi Yuan, Ralf M. Haefner
MODVIS Workshop
No abstract provided.
Using Neural Networks To Better Understand Static And Dynamic Components Of Facial Expression Recognition, Yi-Fan Li, Anne Bibiana Sereno
Using Neural Networks To Better Understand Static And Dynamic Components Of Facial Expression Recognition, Yi-Fan Li, Anne Bibiana Sereno
MODVIS Workshop
Facial expressions are crucial social information for human communication. In the real world, facial expressions are dynamic; however, much of existing research in facial expressions relies on static stimuli. This static approach may limit the ecological validity of our understanding of the emotional information on faces. Our study aims to investigate the dynamic and static information contained in different emotional categories of facial expressions (e.g., happy, sad). Four convolutional neural networks are introduced as models to be trained with a large-scale dynamic facial expression dataset (short videoclips of 16 frames of 7 different emotional categories) in four different ways: ordered …
Modeling Effects Of Edge Classification And Perceptual Organization On The Appearance Of Disk/Annulus Stimuli, Michael E. Rudd
Modeling Effects Of Edge Classification And Perceptual Organization On The Appearance Of Disk/Annulus Stimuli, Michael E. Rudd
MODVIS Workshop
In classical theories of sensory psychophysics, appearance matches conducted with simple stimuli consisting of disks surrounded by annuli were posited to probe low-level contrast mechanisms in the visual pathways that computed the approximate luminance ratio between the disk and annulus for the purpose of achieving lightness constancy (Wallach, 1948). Subsequent models have explained such matches on the basis of neural edge integration (Shapley & Reid, 1985; Rudd & Zemach, 2004). Here, I demonstrate that appearances matches made in the disk/annulus paradigm are influenced by at least two types of top-down effects involving edge classification and perceptual organization. I present a …
Fixational Eye Movements Shake Up The Stationary View On Pattern Vision, Lynn Schmittwilken, Marianne Maertens
Fixational Eye Movements Shake Up The Stationary View On Pattern Vision, Lynn Schmittwilken, Marianne Maertens
MODVIS Workshop
No abstract provided.
Euclidean Coordinates Are The Wrong Prior For Models Of Primate Vision, Garrison W. Cottrell
Euclidean Coordinates Are The Wrong Prior For Models Of Primate Vision, Garrison W. Cottrell
MODVIS Workshop
Convolutional Neural Networks (CNNs) are currently the best models we have of the ventral temporal lobe – the part of cortex engaged in recognizing objects. They have been effective at predicting the firing rates of neurons in monkey cortex, as well as fMRI and MEG responses in human subjects. They are based on several observations concerning the visual world: 1) pixels are most correlated with nearby pixels, leading to local receptive fields; 2) stationary statistics – the statistics of image pixels are relatively invariant across the visual field, leading to replicated features 3) objects do not change identity depending on …
Do Mechanisms Of Sinusoidal Contrast Sensitivity Account For Edge Sensitivity?, Lynn Schmittwilken, Felix A. Wichmann, Marianne Maertens
Do Mechanisms Of Sinusoidal Contrast Sensitivity Account For Edge Sensitivity?, Lynn Schmittwilken, Felix A. Wichmann, Marianne Maertens
MODVIS Workshop
No abstract provided.
Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens
Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens
MODVIS Workshop
No abstract provided.
A Signal Detection Model For The Analysis Of Continuous Response Gradients And An Application To Confidence Rating Data, Fabian A. Soto
A Signal Detection Model For The Analysis Of Continuous Response Gradients And An Application To Confidence Rating Data, Fabian A. Soto
MODVIS Workshop
see attached
Modelling Pairwise Comparisons For Thurstonian Scaling And Kendall Rank Correlation, Maarten Wijntjes
Modelling Pairwise Comparisons For Thurstonian Scaling And Kendall Rank Correlation, Maarten Wijntjes
MODVIS Workshop
Pairwise comparisons are a simple and effective way to measure the relative ordering of two samples. For more than two samples, a global ordering can be constructed and with sufficient data it is possible to construct a metric scale, for example by using Thurstonian scaling. We will discuss the concept of Number of Distinguishable Levels (NDLs) that emerges from a Thurstonian scaling procedure. The NDL is the attribute range in terms of Just Noticeable Differences (JNDs), for example the number of grayscale values. The NDL is either limited by the visual system or by the range of stimuli. The latter …
Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge
Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge
MODVIS Workshop
No abstract provided.
Modeling The Spread Of Object-Based Attention During Free Viewing, Nicolas Roth, Olga Shurygina, Flora Marleen Muscinelli, Klaus Obermayer, Martin Rolfs
Modeling The Spread Of Object-Based Attention During Free Viewing, Nicolas Roth, Olga Shurygina, Flora Marleen Muscinelli, Klaus Obermayer, Martin Rolfs
MODVIS Workshop
No abstract provided.
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
MODVIS Workshop
Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …
Object Rigidity: Competition And Cooperation Between Motion-Energy And Feature- Tracking Mechanisms And Shape-Based Priors, Akihito Maruya, Qasim Zaidi Dr.
Object Rigidity: Competition And Cooperation Between Motion-Energy And Feature- Tracking Mechanisms And Shape-Based Priors, Akihito Maruya, Qasim Zaidi Dr.
MODVIS Workshop
No abstract provided.
Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming
Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming
MODVIS Workshop
No abstract provided.
Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat
Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat
MODVIS Workshop
No abstract provided.
Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke
Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke
MODVIS Workshop
No abstract provided.
Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni
Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni
MODVIS Workshop
We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …
Feature Tracking And Geometrical Priors Counteract Illusory Non-Rigidities From Outputs Of Motion-Energy Cells, Akihito Maruya, Qasim Zaidi
Feature Tracking And Geometrical Priors Counteract Illusory Non-Rigidities From Outputs Of Motion-Energy Cells, Akihito Maruya, Qasim Zaidi
MODVIS Workshop
No abstract provided.
Perceived Object Motion Variance Across Optical Contexts, Jan Jaap R. Van Assen, Mitchell J.P. Van Zuijlen, Shin'ya Nishida
Perceived Object Motion Variance Across Optical Contexts, Jan Jaap R. Van Assen, Mitchell J.P. Van Zuijlen, Shin'ya Nishida
MODVIS Workshop
No abstract provided.
Modeling The Impacts Of Inter-Display And Inter-Lens Separation On Perceived Slant In Virtual Reality Head-Mounted Displays, Jonathan Tong, Laurie M. Wilcox, Robert S. Allison
Modeling The Impacts Of Inter-Display And Inter-Lens Separation On Perceived Slant In Virtual Reality Head-Mounted Displays, Jonathan Tong, Laurie M. Wilcox, Robert S. Allison
MODVIS Workshop
No abstract provided.
Fixational Eye Movements, Perceptual Filling-In, And Perceptual Fading Of Grayscale Images, Michael E. Rudd
Fixational Eye Movements, Perceptual Filling-In, And Perceptual Fading Of Grayscale Images, Michael E. Rudd
MODVIS Workshop
No abstract provided.
Constraining Computational Models Of Brightness Perception: What’S The Right Psychophysical Data?, Guillermo Aguilar, Joris Vincent, Marianne Maertens
Constraining Computational Models Of Brightness Perception: What’S The Right Psychophysical Data?, Guillermo Aguilar, Joris Vincent, Marianne Maertens
MODVIS Workshop
No abstract provided.
Modeling Perceptual Grouping Strategies In Visual Search Tasks, Maria Kon, Gregory Francis
Modeling Perceptual Grouping Strategies In Visual Search Tasks, Maria Kon, Gregory Francis
MODVIS Workshop
No abstract provided.
Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos
Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos
MODVIS Workshop
No abstract provided.
The Fluid Representations Of Networks Estimating Liquid Viscosity, Jan Jaap R. Van Assen, Shin'ya Nishida, Roland W. Fleming
The Fluid Representations Of Networks Estimating Liquid Viscosity, Jan Jaap R. Van Assen, Shin'ya Nishida, Roland W. Fleming
MODVIS Workshop
No abstract provided.
Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
MODVIS Workshop
No abstract provided.
Modelling Human Perception Of High Gloss Materials Using Neural Networks, Konrad E. Prokott, Hideki Tamura, Roland W. Fleming
Modelling Human Perception Of High Gloss Materials Using Neural Networks, Konrad E. Prokott, Hideki Tamura, Roland W. Fleming
MODVIS Workshop
No abstract provided.